My first blog post that I ever wrote was about a technique for process automation that I called “Intelligent License Plates” or ILP for short. The technology was conceived as an effort to combine efficient operational processes with tracking of collections of objects within the context of the operator. The basic premise was to develop a bar-coded collection of objects that when scanned, would be able to take actions based on your context and the state of the License Plate (LP). The action could be completely different depending on where they scan it, when they scan it, the user, and what the “state” is of the LP within a workflow. As you might imagine, supporting fast flexible access to a lot of metadata surrounding an LP is challenging. This is where the strength of Search comes in.

This article will spend some time expanding on Search; another noteworthy benefit from ILP’s.

Search brings the warehouse management system benefits in dealing with the challenge of flexibility and supporting various workflows that are very different within an agile distribution or manufacturing organization. These benefits come in the form of efficient and timely discovery of LP attributes or the content within them as part of a successful ILP design.

As I mentioned in my first article, License Plates are a hierarchical collection of inventory items. These inventory items come in untold varieties. The most obvious attribute for an LP and its contents, is support for lot numbers and serial numbers. Both of these attributes are similar in nature, but the storage required for serial numbers and lot number could potentially be optimized quite differently. For example, some implementations of ILP can make the assumption that the contents of the LP are always the same item, same lot number. With this rule established, the application can speed operations by only searching attributes at the LP level when accessing a particular item lot number.

With Search, we can allow unique process flows to retrieve attributes in a loose way by “searching” against these attributes.

Given the wide variety of data stored within these searchable attributes, the design deals with 3 types of attribute storage:

1) Vertical data (many columns returned as 1 row of data)

2) Horizontal data (few columns that store large volumes of rows)

3) Compressed data (few columns and minimal rows)

If we assume 1 Lot Number per LP, then we can optimize our storage as “Compressed data” since we can store the item lot number as a single attribute at the LP level.

On the opposite side of the spectrum 10,000 Serial Numbers per LP requires a completely different optimization as it would be classified as horizontal data. The point I am trying to make is this; Search provides maximum flexibility for both humans and applications to communicate information due to its loosely coupled nature and “black box” approach. When it comes to developing new generations of warehouse management systems, look for Search to not just be used for finding that pallet that had the oldest expiration date, but to radically change the design of these types of applications.